Image Captioning Using Deep Learning
C. S. Kanimozhiselvi, V Karthika, S P Kalaivani, S Krithika
Abstract
The process of generating a textual description for images is known as image captioning. Now a days it is one of the recent and growing research problem. Day by day various solutions are being introduced for solving the problem. Even though, many solutions are already available, a lot of attention is still required for getting better and precise results. So, we came up with the idea of developing a image captioning model using different combinations of Convolutional Neural Network architecture along with Long Short Term Memory in order to get better results. We have used three combination of CNN and LSTM for developing the model. The proposed model is trained with three Convolutional Neural Network architecture such as Inception-v3, Xception, ResNet50 for feature extraction from the image and Long ShortTerm Memory for generating the relevant captions. Among the three combinations of CNN and LSTM, the best combination is selected based on the accuracy of the model. The model is trained using the Flicker8k dataset.